Method and system for crystal identification

ABSTRACT

Some embodiments of the present disclosure relates to a method and system for generating crystal lookup table (CLT) based on a flood histogram. The method may include receiving a flood histogram of a subject; determining a crystal central position map based on the flood histogram, the crystal central position map including a plurality of crystal central positions, forming rows and columns of the plurality of crystal central positions to generate a labelled crystal lookup table, forming a template based on the rows and the columns in the labelled crystal central position map, and correcting the labelled crystal central position map based on the template and the flood histogram to obtain a corrected crystal central position map. A crystal lookup table may be formed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application Nos. CN201510214482.5 filed on Apr. 29, 2015, and CN 201510581907.6, filed onSep. 14, 2015. The contents of each of which are incorporated hereby byreference.

TECHNICAL FIELD

This application generally relates to a method and system for imageprocessing of an imaging system with a positron emission tomography(PET) detector/scanner, and more particularly, this application relatesto crystal identification.

BACKGROUND

Crystal identification may be performed for a single imaging modalitysystem and/or a multi-modality imaging system with a positron emissiontomography (PET) detector/scanner, especially for a high-resolution PETdevice. PET uses a large number of scintillator crystals (or referred toas crystals for brevity) of a small size in its detector blocks. Thesmall size of the crystals may result in low signal-to-noise ratio(SNR); the large number of the crystals may lead to distortion ofcrystal array during signal encoding/decoding process, making itchallenging to identify crystals in a PET detector.

Crystal identification may involve segmentation of a flood histograminto regions equal to the total number of scintillator crystals in thedetector array, such that each region has one peak. A peak maycorrespond to the central position of the distribution of the eventsdetected in one crystal. A region with a peak may correspond to thelocation and the size of the crystal. Existing segmentation schemes arederived from a broad range of image processing and pattern recognitiontechniques. A relatively straightforward scheme is to manually click onpeak locations on a computer screen and then segment the individualregions, which is labor intensive and time-consuming. Thus, there existsa need in the field to provide a method and system for crystalidentification that may address these and other technical challenges.

SUMMARY

One aspect of the present disclosure relates to a method and system forgenerating and/or correcting a crystal central position map (CCPM) basedon a flood histogram. The method may include one or more of thefollowing operations. The flood histogram of a subject may be received.An initial crystal central position map (CCPM) may be determined basedon the flood histogram. The initial crystal central position map mayinclude a plurality of crystal central positions. Rows and columns ofthe plurality of crystal central positions may be formed to generate alabelled crystal central position map. A template may be formed based onthe rows and the columns in the labelled crystal central position map.The labelled crystal central position map may be corrected based on thetemplate and the flood histogram to obtain a corrected crystal centralposition map.

Another aspect of the present disclosure relates to a non-transitorycomputer readable medium comprising executable instructions. Theinstructions, when executed by at least one processor, may cause the atleast one processor to effectuate a method for generating and/orcorrecting a CCPM based on a flood histogram.

In some embodiments, to determine the crystal central position map basedon the flood histogram may include one or more of the followingoperations. A zero-one normalization may be performed on the floodhistogram to generate a normalized flood histogram I_0. K iterations ofdecay mask treatment may be performed on I_0 to generate a series ofintermediate flood histogram I_i, 1≦i≦K. For each of the intermediateflood histogram I_i, a threshold determination may be performed on theintermediate flood histogram I_i to generate a binary image B_i. Thecrystal central position map may be generated based on a group includingone or more of the K binary images B_i, 1≦i≦K. In some embodiments, thezero-one normalization on a flood histogram may include one or more ofthe following operations: a linear transformation may be performed onthe flood histogram to obtain a transformed flood histogram such thatthe intensities of pixels in the transformed flood histogram fall in therange between zero and one. In some embodiments, the decay masktreatment on an image may include multiplying the image by thenormalized flood histogram I_0.

In some embodiments, the threshold determination on an intermediateflood histogram I_i may be performing an Otsu thresholding algorithm onI_i.

In some embodiments, a crystal central position map may be generatedbased on one or more of the following operations. An initial collectionof connected components C is set to be void, i.e., empty. Then for eachB_i=K, K−1, . . . , 2, 1, the set of connected components in B_i isidentified, and classified as overlapping or non-overlapping withrespect to C. Those non-overlapping connected components in B_i arecollected into C. By performing the operations above, the collection ofconnected components C may grow larger as the index i decreases from Kto 1. In some embodiments, a crystal central position map may begenerated based on C, by calculating the center of mass of eachconnected component in C, and collecting the centers of mass ofconnected components in C as the crystal central position map.

In some embodiments, the crystal central position map may be determinedbased on calculation of local maximum gray level of the flood histogram.

In some embodiments, rows and columns may be formed based on one or moreof the following operations. Hough transformation may be performed onthe flood histogram to generate a transformed image. A configurationtemplate may be selected. For a first crystal central position of theplurality of crystal central positions, one or more candidate crystalcentral positions may be identified based on the selected configurationtemplate and the transformed image. The one or more candidate crystalcentral positions may be marked as being in a same row or a same columnas the first crystal central position.

In some embodiments, the labelled crystal central position map may becorrected based on the template by iteratively regularizing thetemplate.

In some embodiments, a crystal lookup table may be generated based on,for example, a CCPM, a labelled CCPM, or a corrected CCPM. In someembodiments, the crystal lookup table may be generated by delineatingboundary lines separating a plurality of crystal central positions basedon the corrected crystal central position map. The delineating boundarylines may be performed by dynamic programming. The delineating boundarylines may include substantially minimizing cumulative energy of theboundary lines.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the present disclosure is further described in termsof exemplary embodiments. These exemplary embodiments are described indetail with reference to the drawings. These embodiments arenon-limiting examples, in which like reference numerals representsimilar structures throughout the several views of the drawings, andwherein:

FIG. 1 illustrates an exemplary image processing system according tosome embodiments of the present disclosure;

FIG. 2 illustrates an exemplary processor according to some embodimentsof the present disclosure;

FIG. 3 illustrates an exemplary a crystal lookup table module accordingto some embodiments of the present disclosure;

FIG. 4 illustrates an exemplary crystal central position map generatoraccording to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for imageprocessing according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for generating acrystal central position map (CCPM) according to some embodiments of thepresent disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for labellingrows and columns in a CCPM according to some embodiments of the presentdisclosure;

FIG. 8 is a flowchart illustrating an exemplary process for labellingrows and columns in a CCPM according to some embodiments of the presentdisclosure;

FIG. 9A is a flowchart illustrating an exemplary process for generatinga CCPM according to some embodiments of the present disclosure;

FIG. 9B illustrates exemplary candidate crystal central positionsproduced by successive non-maximum decay (SNMD) according to someembodiments of the present disclosure;

FIG. 10 illustrates a flowchart of an exemplary process for delineatingthe boundaries between crystals according to some embodiments of thepresent disclosure;

FIG. 11 is a flowchart illustrating an exemplary process for generatingCLTs based on fusions of CCPMs generated by different methods accordingto some embodiments of the present disclosure;

FIG. 12A is a flowchart illustrating an exemplary process for generatingCCPMs based on fusions of CCPMs generated by different methods accordingto some embodiments of the present disclosure;

FIG. 12B illustrates three exemplary confidence criterions based oncrystal positions according to some embodiments of the presentdisclosure;

FIG. 12C illustrates the second class of CCPs according to someembodiments of the present disclosure;

FIG. 12D illustrates different CCPs before and after correctionaccording to some embodiments of the present disclosure;

FIG. 13A illustrates an exemplary flowchart of a process for producing acrystal lookup table (CLT) from a flood histogram according to someembodiments of the present disclosure;

FIG. 13B illustrates an exemplary flood histogram according to someembodiments of the present disclosure;

FIG. 13C illustrates an exemplary initial CCPM according to someembodiments of the present disclosure;

FIG. 13D illustrates an exemplary transformed flood histogram in polarcoordinates according to some embodiments of the present disclosure;

FIG. 13E illustrates an exemplary intensity projection diagram accordingto some embodiments of the present disclosure;

FIG. 13F illustrates an exemplary identification of the row and thecolumn of crystal central positions on which a chosen crystal centralposition lies according to some embodiments of the present disclosure;

FIG. 13G illustrates an exemplary distribution of rows in the labelledCCPM according to some embodiments of the present disclosure;

FIG. 13H illustrates an exemplary distribution of columns in thelabelled CCPM according to some embodiments of the present disclosure;

FIG. 13I illustrates an exemplary template according to some embodimentsof the present disclosure; and

FIG. 13J illustrates an exemplary crystal lookup table and theassociated boundary lines within the crystal lookup table according tosome embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that some embodiments of the present disclosure may bepracticed without such details. In other instances, well known methods,procedures, systems, components, and/or circuitry have been described ata relatively high-level, without detail, in order to avoid unnecessarilyobscuring aspects of some embodiments of the present disclosure. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of some embodiments of the present disclosure.Thus, some embodiments of the present disclosure is not limited to theembodiments shown, but to be accorded the widest scope consistent withthe claims.

It will be understood that the term “system,” “engine,” “module,”“unit,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevels in ascending order. However, the terms may be displaced by otherexpression if they may achieve the same purpose.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to” or “coupled to” anothersystem, unit, engine, module, or block, it may be directly on, connectedor coupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

The terminology used herein is for the purposes of describing particularexamples and embodiments only, and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include,”and/or “comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

Provided herein are systems and components for non-invasive biomedicalimaging, such as for disease diagnostic or research purposes. The systemmay include a single imaging modality or multiple imaging modalities forconducting different medical scans or studies, including but not limitedto a positron emission tomography (PET), an ultrasound scanning (US), acomputed tomography (CT), a digital radiography (DR), a single photonemission computed tomography (SPECT) and a magnetic resonance imaging(MRI).

The multi-modality imaging system of some embodiments of the presentdisclosure may include a PET scanner and one or more additional imagingmodalities, such as PET-MR, PET-US, PET-CT, etc. In a multi-modalitysystem, the mechanisms through which different imaging modalitiesoperate or function may be the same or different. Accordingly, theimaging information may also be the same or different. For example, insome embodiments, the imaging information may be internal and/orexternal information, and may be functional and/or structuralinformation of a subject. Particularly, in some embodiments, the imaginginformation of different modalities may complement one another, therebyproviding a set of imaging data describing the subject from differentanalytical angles. For example, in some embodiments, the multi-modalityimaging may achieve the merging of morphological and functional images.

The term “subject” as used herein broadly relates to any organic orinorganic mass, natural or man-made, that has a chemical, biochemical,biological, physiological, biophysical and/or physical activity orfunction. Exemplary embodiments of the subject pertaining to someembodiments of the present disclosure include cells, tissues, organs orwhole bodies of human or animal. Other exemplary embodiments include butnot limited to a man-made composition of organic and/or inorganicmatters that are with or without life.

The above types of imaging modalities that may be included in thepresent system are not exhaustive and are not limiting. After consultingsome embodiments of the present disclosure, one skilled in the art mayenvisage numerous other changes, substitutions, variations, alterations,and modifications without inventive activity, and it is intended thatsome embodiments of the present disclosure encompasses all such changes,substitutions, variations, alterations, and modifications as fallingwithin its scope.

In some embodiments of the present disclosure, the multi-modalityimaging system may further include modules and components for performingpositron emission tomography (PET) imaging and analysis.

This is understood that the following descriptions are provided inconnection with medical image processing for illustration purposes andnot intended to limit the scope of some embodiments of the presentdisclosure. The image processing disclosed herein may be used forpurposes other than medical treatment or diagnosis. For instance, theimage processing may be used for purposes of detecting a fracture withina structure or its progression over time, a non-uniform portion within apiece of material, etc.

For illustration purposes, the following description is provided to helpbetter understanding an image processing. It is understood that this isnot intended to limit the scope of some embodiments of the presentdisclosure. For persons having ordinary skills in the art, a certainamount of variations, changes and/or modifications may be deducted underguidance of some embodiments of the present disclosure. However, thosevariations, changes and/or modifications do not depart from the scope ofsome embodiments of the present disclosure.

Some embodiments of the present disclosure relates to positron emissiontomography. Specifically, some embodiments of the present disclosurerelates to a method and system for crystal identification. The processof crystal identification as illustrated in some embodiments of thepresent disclosure may be automated, or semi-automated. It may beimplemented in a computer-aided and automated medical diagnosis and/ortreatment system.

FIG. 1 illustrates an exemplary embodiment of a medical image processingsystem according to some embodiments of the present disclosure. As shownin the figure, the medical image processing system may include a PETscanner 110, a processor 120 and a network 130.

The PET scanner 110 may be used to examine a subject 113. The PETscanner 110 may include a PET and/or a PET detector. The PETscanner/detector may include a plurality of detector blocks. Thedetector block may include a plurality of detector arrays. The detectorarray may include an array of crystals 112. A crystal 112 may be coupledto a photomultiplier tube (PMT) 111 via a light guide. The crystals 112may be arranged in the form of a matrix.

The processor 120 may process different kinds of information receivedfrom the PET scanner 110, the network 130, or the like, or anycombination thereof. The information may include data, such as a number,a text, an image, a voice, a force, a model, an algorithm, a software, aprogram, or the like, or any combination thereof. For example, theinformation may include a subject information, an operator information,an instrument information, an instruction information, a PET deviceinformation, or the like, or any combination thereof. In someembodiments, the subject information may include ethnicity, citizenship,religion, gender, age, matrimony, height, weight, medical history, job,personal habits, organ, tissue, or the like, or any combination thereof.The operator information may include a department, a title, anexperience, an operating history, or the like, or any combinationthereof. The instrument information may include running status, serialnumber, operation date, etc. of the image processing system. Theinstruction information may include, for example, a control command, anoperation command, etc. of the image processing system. Merely by way ofexample, the command for selecting images may be an instruction toselect one or more images for detecting changes in images. The PETdevice information may include product information, such asmanufacturer, type, some information of the PET device, the PET scanneror the PET detector, the detector, the crystals, or the like, or anycombination thereof.

The processor 120 may perform pre-processing, generate a crystal centralposition map (CCPM), generate a crystal lookup table (CLT), performimage processing based on CLT, or the like, or any combination thereof.In some embodiments, the pre-processing may relate to image processingincluding, e.g., crystal identification.

The processor 120 may be a computer, a laptop, a cell phone, a mobilephone, a portable equipment, a pad, a Central Processing Unit (CPU), anApplication-Specific Integrated Circuit (ASIC), an Application-SpecificInstruction-Set Processor (ASIP), a Graphics Processing Unit (GPU), aPhysics Processing Unit (PPU), a Digital Signal Processor (DSP), a FieldProgrammable Gate Array (FPGA), a Programmable Logic Device (PLD), aController, a Microcontroller unit, a Processor, a Microprocessor, anARM, or the like, or any combination thereof.

The network 130 may establish connection between the PET scanner 110 andthe processor 120. The network 130 may be a single network or acombination of different networks. For example, the network 130 may be alocal area network (LAN), a wide area network (WAN), a public network, aprivate network, a proprietary network, a Public Telephone SwitchedNetwork (PSTN), a Bluetooth, the Internet, a wireless network, a virtualnetwork, or the like, or any combination thereof.

It should be noted that the imaging system described above is providedfor illustration purposes, and not intended to limit the scope of someembodiments of the present disclosure. Apparently for persons havingordinary skills in the art, numerous variations and modifications may beconducted under the teaching of some embodiments of the presentdisclosure without inventive activity. Some embodiments of the presentdisclosure is intended to encompass all those variations andmodifications as falling under its scope. In some embodiments, thecrystals 112 may be scintillation crystals. In some embodiments, thephotomultiplier tubes (PMTs) 111 may be replaced by one or moreposition-sensitive avalanche photodiodes (PSAPDs). In some embodiments,the processor 120 may be connected to an external memory. In someembodiments, the processor 120 may be a server, which may implementand/or store some information and/or process relating to imageprocessing. Merely by way of example, the server may be a cloud server,which may provide computation capacity, storage capacity, or the like,or a combination thereof.

FIG. 2 illustrates an exemplary block diagram of the processor 120according to some embodiments of the present disclosure. As shown in thefigure, the processor 120 may include a pre-processing module 210, acrystal lookup table module 220, an image processing module 230, astorage module 240 and a control module 250. Each module may connectwith other modules or one of other modules. The connection betweendifferent modules may be wired or wireless. The wired connection mayinclude using a metal cable, an optical cable, a hybrid cable, aninterface, or the like, or any combination thereof. The wirelessconnection may include using a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC),a Wi-Fi, a Wireless a Wide Area Network (WWAN), or the like, or anycombination thereof.

According to some embodiments of the present disclosure, thepre-processing module 210 may perform pre-processing relating to crystalidentification, image processing, etc. For instance, a flood histogrammay be pre-processed by the pre-processing module 210 to generateintermediate flood histogram. In some embodiments, the intermediateflood histogram may be a 2D flood histogram of one or more crystals, ora 2D flood histogram of one or more blocks of crystals. Thepre-processing may be histogram equalization, de-noising, peakenhancement, or the like, or any combination thereof. De-noising and/orpeak enhancement may be based on low-pass filtering, high-passfiltering, local maxima filtering, etc. The pre-processing may enhancethe contrast of peaks and/or valleys using top-hat and bottom-hattransformations, which may be based on a morphology method. In someembodiments, the pre-processing may be an image smoothing by applying aGaussian kernel, whose standard deviation may be set to be one third ofthe shortest distance between adjacent peaks in the X or Y direction. Insome embodiments, Contrast-Limited Adaptive Histogram Equalization(CLAHE) may be used to remove the global intensity inhomogeneity andincrease the local contrast of the image. CLAHE may homogenize thedistribution of intensity of different spots.

According to some embodiments of the present disclosure, the crystallookup table module 220 may generate a crystal lookup table (CLT). Acrystal central position map (CCPM) that indicates the locations ofcrystal centers may be generated before the crystal lookup table isgenerated. The CCPM may be generated from a flood histogram, alsoreferred to as a characteristic flood field response. For example, theCCPM may be generated by locating the local maximum of intensity withinthe flood histogram.

In some embodiments, the CCPM may be generated based on the analysis ofconnected components of a set of binary images generated from a 2D floodhistogram. In a binary image, the connected component may refer to aconnected region inside which all pixel values are one.

A set of binary images may be generated based on a set of intermediateflood histograms, which may be generated by iterative multiplicationswith a normalized image. The normalized image may be generated bynormalization of a flood histogram. The normalized image may be used asa decay mask. The decay mask may be iteratively multiplied with itselfto provide multiple intermediate flood histograms. Specifically, a decaymask treatment on an image may be given by multiplying the image by thedecay mask. The connected components of the binary image may begenerated to determine centers of connected components. A CCPM may begenerated based on a judgment whether the connected components overlap.

The crystal central positions inside the CCPM may be labelled byassigning row indices and column indices to the crystal centralpositions. Such a CCPM with labelled crystal central positions may bereferred to as a labelled CCPM. In some embodiments, the crystal centralposition map (CCPM), labelled or not, may be further corrected. In someembodiments, one or more kinds of artifacts may be removed from thelabelled CCPM.

Based on a CCPM (e.g., a labelled CCPM, a corrected CCPM, etc.), acrystal lookup table (CLT) may be generated by delineating theboundaries between crystals in the CCPM. In some embodiments, thecrystal lookup table may be generated based on the fusion of differentCCPMs, as described elsewhere in the present disclosure.

According to some embodiments of the present disclosure, the imageprocessing module 230 may perform processing relating to image or datafrom the crystal lookup table module 220, the storage module 240, and/orthe control module 250. As used herein, data from the crystal lookuptable may be in the form of an image. The image processing module 230may perform post-processing of crystal identification. For example, insome embodiments, some corrections may be performed by manually addingand/or editing peaks on a flood histogram. Such operations may beprovided by different image processing method including, for example, aMATLAB GUI tool.

According to some embodiments of the present disclosure, the storagemodule 240 may store information relating to crystal identificationand/or image processing. The storage module 240 may acquire informationfrom one or more other modules or output information to one or moreother modules. Merely by way of example, the storage module 240 mayreceive and store a crystal lookup table, which may be generated fromthe crystal lookup table module 220, and then convey it to the imageprocessing module 230. This process may be coordinated and/or controlledby the control module 250. The information stored in the storage module240 may be acquired from or output to an external storage deviceincluding, for example, a floppy disk, a hard disk, a CD-ROM, a networkserver, a cloud server, a wireless terminal, or the like, or anycombination thereof.

The storage module 240 may store information by way of electric energy,magnetic energy, optical energy, or a virtual storage resource, etc. Thestorage module 240 that may store information by way of electric energymay include Random Access Memory (RAM), Read Only Memory (ROM), flashmemory, or the like, or any combination thereof. The storage module 240that may store information by way of magnetic energy may include a harddisk, a floppy disk, a magnetic tape, a magnetic core memory, a bubblememory, a USB flash drive, or the like, or any combination thereof. Thestorage module 240 that may store information by way of optical energymay include CD (Compact Disk), VCD (Video Compact Disk), or the like, orany combination thereof. The storage module 240 may store information byway of virtual storage resources. The storage module 240 may includecloud storage, a virtual private network, and/or other virtual storageresources. The method to store information may include sequentialstorage, link storage, hash storage, index storage, or the like, or anycombination thereof.

According to some embodiments of the present disclosure, the controlmodule 250 may control the pre-processing module 210, the crystal lookuptable module 220, the image processing module 230, and the storagemodule 240 in the processor 130 to accomplish crystal identificationand/or the generation of a CCPM or a CLT. For example, the controlmodule 250 may receive signals and/or instructions from or sendinformation to the pre-processing module 210, the crystal lookup tablemodule 220, the image processing module 230, and the storage module 240.

It should be noted that the imaging system described above is providedfor the purposes of illustration, and not intended to limit the scope ofsome embodiments of the present disclosure. Apparently for personshaving ordinary skills in the art, numerous variations and modificationsmay be conducted under the teaching of some embodiments of the presentdisclosure without inventive activity. Some embodiments of the presentdisclosure is intended to encompass all those variations andmodifications as falling under its scope. For example, in someembodiments, these modules may be independent. In some embodiments, partof the modules may be integrated into one module to work together.Merely by way of example, the functioning of the control module 250 maybe realized in the crystal lookup table module 220. In some embodiments,the image processing module 230 may perform the fusion of several CCPMsor several crystal lookup tables.

FIG. 3 illustrates an exemplary block diagram of crystal lookup tablemodule 220 according to some embodiments of the present disclosure. Asshown in the figure, the crystal lookup table module 220 may include acrystal central position map generator 310 and a crystal lookup tablegenerator 320. The crystal central position map generator 310 maygenerate a crystal central position map. In some embodiments, thecrystal central position map generator 310 may include one or morecrystal central position map sub-generators. Different crystal centralposition map sub-generators may generate one or more crystal centralposition maps (CCPMs) by different methods.

According to some embodiment of the present disclosure, the crystalcentral position map sub-generator 311 may generate one or more CCPMsbased on the method described in FIG. 6 and FIG. 7. In some embodiments,a CCPM may be generated from a flood histogram. A labelled CCPM may begenerated by assigning row indices and column indices to crystal centralpositions in the CCPM. A template may be generated from the labelledCCPM. The crystal central position map (CCPM) may be corrected based onthe template. In some embodiments, one or more kinds of artifacts may beremoved from the labelled CCPM.

According to some embodiments of the present disclosure, the crystalcentral position map sub-generator 312 may generate one or more CCPMsbased on the method described about FIG. 6 and FIG. 8. In someembodiments, a CCPM may be generated from a flood histogram. A labelledCCPM may be generated by assigning row indices and column indices tocrystal central positions in the CCPM. A template may be generated fromthe labelled CCPM. The crystal central position map (CCPM) may becorrected based on the template. In some embodiments, one or more kindsof artifacts may be removed from the labelled CCPM. In processing ofassigning row indices and column indices to crystal central positions inthe CCPM, a flood histogram may be subject to Hough transformation togenerate a transformed image. A reference row and a reference column ofthe transformed image may be labelled, and a labelled CCPM based on thereference row and the column may be generated.

According to some embodiments of the present disclosure, the crystalcentral position map sub-generator 313 may generate one or more CCPMsbased on the methods described in FIG. 7 and FIG. 9A. For purposes ofillustration, the CCPM may be generated based on the connectedcomponents of a binary image generated from a 2D flood histogram. Forinstance, a normalized image may be generated by normalizing the floodhistogram. The normalized image may be used as a decay mask. The decaymask may be iteratively multiplied with itself to provide multipleprocessed images. A set of binary images may be generated based on theprocessed images. The connected components of the set of binary imagesmay be generated to determine centers of connected components. A CCPMmay be generated based on a judgment whether the connected componentsoverlap.

According to some embodiments of the present disclosure, the crystallookup table generator 320 may generate a CLT. The CLT may be generatedbased on the CCPM obtained from the crystal lookup table module 220. TheCLT may be generated by delineating the boundaries between crystals inthe CCPM. The delineation may be achieved by segmenting the CCPM intoregions. The number of regions may be equal to the total number ofcrystals in the detector arrays. In some embodiments, the crystal lookuptable generator 320 may include a fusion unit 321 and a segmentationunit 322. In some embodiments, the fusion unit 321 may fuse CCPMsgenerated by different methods to generate a new CCPM. Various methodsfor CCPM generation may be found in, for example, FIGS. 6-9 and thedescription thereof, and elsewhere in the present disclosure, and thelike. In some embodiments, different CCPMs may be generated by, forexample, different crystal central position map sub-generators 311, 312,and 313. In some embodiments, the segmentation unit 322 may delineatethe boundaries between crystals in a CCPM.

It should be noted that the imaging system described above is providedfor the purpose of illustration, and not intended to limit the scope ofsome embodiments of the present disclosure. Apparently for personshaving ordinary skills in the art, numerous variations and modificationsmay be conducted under the teaching of some embodiments of the presentdisclosure without inventive activity. Some embodiments of the presentdisclosure is intended to encompass all those variations andmodifications as falling under its scope. For example, in someembodiments, the crystal central position map generator 310 and thecrystal lookup table generator 320 may be integrated into one generator.In some embodiments, there may be more than three crystal centralposition map sub-generators in the crystal central position mapgenerator 310. In some embodiments, the crystal central position mapsub-generators 311, 312 and 313 may be integrated into one generator. Insome embodiments, there may be no fusion unit 321 or/and segmentationunit 322 in the crystal lookup table generator 320. In some embodiments,the fusion unit 321 and the segmentation unit 322 may be integrated intoone unit.

FIG. 4 illustrates an exemplary block diagram of a crystal centralposition map generator according to some embodiments of the presentdisclosure. As shown in the figure, the central position mapsub-generator 311 may include a positioning unit 410, and a labellingunit 420.

According to some embodiments of the present disclosure, the positioningunit 410 may identify the position of a crystal. The flood histogramprocessed by the positioning unit 410 may be received from the PETscanner 110, the storage module 240, the labelling unit 420, a storagedevice (e.g., a floppy disk, a hard disk, a wireless terminal, acloud-based storage device, etc.), etc. The positioning of a crystal mayinclude the crystal central position, row index, column index, crystalboundary position, or the like, or any combination thereof. In someembodiments, the positioning unit 410 may identify a crystal centralposition (CCP). For example, peak detection and peak labelling may beused to identify the CCP. For example, successive non-maximum decay(SNMD) may be used to detect peaks; spline fitting may be used to labelor identify rows and columns of the peaks. As used herein, a peak maycorrespond to a crystal. In some embodiments, the positioning unit 410may delineate the boundary of a crystal. The positioning unit 410 maygenerate CCPMs, or boundaries designating the positions of crystals, thelike, or any combination thereof.

According to some embodiments of the present disclosure, the labellingunit 420 may correct images or relevant data to obtain more realisticimages. The images or data to be corrected may be received from the PETscanner 110, the pre-processing module 210, the crystal lookup tablemodule 220, the image processing module 230, the storage module 240, thecrystal central position map generator 310, the crystal lookup tablegenerator 320, the positioning unit 410, a storage device (e.g., afloppy disk, a hard disk, a wireless terminal, a cloud-based storagedevice, etc.), etc. The images corrected by the labelling unit 420 mayinclude initial images, the pre-processed images, the post-processedimages, etc. In some embodiments, the labelling unit 420 may removeartifacts from a labelled CCPM. In some embodiments, the labelling unit420 may correct a CCPM based on a template. The labelling unit 420 maygenerate a corrected CCPs and/or a corrected CCPM.

It should be noted that the crystal central position map generatordescribed above is provided for purposes of illustration, and notintended to limit the scope of some embodiments of the presentdisclosure. Apparently for persons having ordinary skills in the art,numerous variations and modifications may be conducted under theteaching of some embodiments of the present disclosure without inventiveactivity. Some embodiments of the present disclosure is intended toencompass all those variations and modifications as falling under itsscope. For example, in some embodiments, the procedure in thepositioning unit 410 and the procedure in the labelling unit 420 mayperform sequentially or concurrently. In some embodiments, thepositioning unit 410 and the labelling unit 420 may be integrated intoone unit or split into more units.

FIG. 5 is a flowchart illustrating an exemplary process for imageprocessing according to some embodiments of the present disclosure.Process 500 may be performed by processing logic including hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device to performhardware simulation), or the like, or a combination thereof. In someimplementations, process 500 may be performed by one or more processingdevices (e.g., processer 130 as described in connection with FIG. 1 andFIG. 2) and elsewhere in the present disclosure.

To obtain a CLT, a flood histogram of the detector block is firstacquired by irradiating the detector with an annihilation photon floodsource. As used herein, a flood histogram may describe a two-dimensionaldistribution of the detected events from the photon detectors. A floodhistogram may contain a plurality of pixels. A crystal may berepresented by a set of connected pixels in the flood histogram. A peakmay correspond to a pixel with the highest intensity for a crystal. Thecrystal central position may or may not correspond to a peak. For a setof connected pixels in the flood histogram, the center of mass of theset of connected pixels may be determined. The center of mass of a setof connected pixels may be designated as a crystal central position. Insome embodiments, the center of mass of a set of connected pixels may bedetermined by calculating the average X coordinate of the pixels in theset of connected pixels, and the average Y coordinate of the pixels inthe set of connected pixels. The average may be an arithmetic average.In some embodiments, the average may be a geometric average.High-resolution PET may include a large number of small-sized crystalsin its detector blocks. The small crystal size may result in a lowsignal-to-noise ratio (SNR), and the large number of crystals may leadto distortion of the crystal array during signal encoding/decodingprocess. Exemplary artifacts may include ghost artifacts, noise,irregular rotations and broken crystals, or the like, or a combinationthereof.

In step 501, a crystal central position map (CCPM) may be generatedbased on a flood histogram. The flood histogram may undergo somepre-processing to improve its quality. The initial flood histogram maybe generated from different PET detectors. In some embodiments, thepre-processing may include histogram equalization, noise reduction, peakenhancement, or the like, or any combination thereof. A CCPM may beobtained by performing crystal identification based on the floodhistogram. In some embodiments, a local maxima method may be applied toidentify crystal central positions. For example, a morphology method anda Gaussian kernel may be applied to enhance the contrast of the floodimage, and peaks in the flood image may be searched and identified. Insome embodiments, a successive non-maximum decay (SNMD) algorithm may beapplied to obtain an initial CCPM. For example, a CCPM may be generatedby iteratively multiplying the flood histogram.

In step 502, a crystal lookup table (CLT) may be generated based on theCCPM. A CLT may be generated by delineating the boundaries betweencrystals in the CCPM. In some embodiments, various techniques including,for example, clustering, curve/grid fitting, etc., may be utilized todelineate boundaries of crystals in a flood histogram. For example,crystals may be identified using a clustering technique based on, forexample, a Gaussian mixture model (GMM), a neural network basedself-organizing feature map (NN-SOFM), Fuzzy C-means (FCM), or the like,or any combination thereof. This may be done by segmenting the floodhistogram of a detector block into regions. The number of regions may beequal to the total number of crystals in that detector block. In someembodiments, a mathematical algorithm of dynamic programming may be usedto produce the crystal boundaries.

In step 503, PET scanner correction may be performed based on theobtained CLT. In some embodiments, more than one CTLs, generated atpossibly different times, may be used to calibrate the PET scanner.

It should be noted that the flowchart described above is provided forthe purposes of illustration, and not intended to limit the scope ofsome embodiments of the present disclosure. For persons having ordinaryskills in the art, various variations and modifications may be conductunder the teaching of some embodiments of the present disclosure.However, those variations and modifications may not depart from theprotecting of some embodiments of the present disclosure.

FIG. 6 is a flowchart illustrating an exemplary process of generatingCCPMs according to some embodiments of the present disclosure. Process600 may be performed by processing logic including hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device to performhardware simulation), or a combination thereof. In some implementations,process 600 may be performed by one or more processing devices (e.g.,crystal lookup table module 220 of FIG. 2) executing the crystal centralposition generator 310 as described in connection with FIGS. 3 and 4above and elsewhere in the present disclosure.

In step 601, a flood histogram may be obtained. The flood histogram maybe received from one scan systems (e.g., system 100 of FIG. 1), one ormore storage modules 240, a storage device (e.g., a floppy disk, a harddisk, a wireless terminal, a cloud-based storage device, etc.), etc.

In a flood histogram, a crystal may be represented by an intensity spot.In an ideal condition, the radiation of a same radiation source isuniform and crystals may be of a same size and shape, and intensityspots in a flood histogram corresponding to the crystals may be the sameand follow an isotropic 2D-Delta-like distribution. In reality, however,different intensity spots may be non-uniform in terms of intensity,orientation, shape, size, or the like, or a combination thereof. Thenon-uniformity may be due to factors including, for example,imperfection in crystal manufacturing, photon scattering effects,nonlinear distortions of position decoding, or the like, or acombination thereof. In some cases, adjacent spots may be merged witheach other, which may be referred to as “merged peaks.” A spot at theedge of a crystal array may appear in two flood histograms relating totwo adjacent detector blocks, which may lead to the generation of ghostartifacts (also referred to as “cross-talk events”).

In step 602, an initial CCPM may be generated based on the floodhistogram. In some embodiments, a local maxima method may be applied toidentify crystal central positions. For example, a morphology method anda Gaussian kernel may be applied to enhance the contrast of the floodhistogram, and peaks in the flood image may be searched and identified.In some embodiments, a crystal peak identification algorithm, such asthe successive non-maximum decay (SNMD) algorithm illustrated in FIG.9A, may be utilized to obtain the initial CCPM.

In step 603, an operation of labelling row index and column index ofeach crystal central position may be performed. In some embodiments,labelling rows and columns of the CCPM may include three operations.Firstly, a crystal central position x may be selected and designated asthe origin in the initial CCPM. Secondly, crystals have the same row andsame column as the crystal central position x may be labelled of theCCPM, and the same row and same column may be set as the reference rowand reference column. Thirdly, other rows and/or columns of crystals maybe labelled based on the reference row and/or the reference column.

In some embodiments, the labelling of rows and/or columns of crystalsmay be proceeded in a progressive way. Merely by way of example, after acertain number, for example, N, of rows of crystal positions have beenlabelled, splines may be utilized to curve fit these N rows of crystalpositions. A crystal central position z that lies above and near the setof N splines may be chosen, and the crystals lying within the same rowas the crystal central position z may be identified and labelled. Asanother example, after a certain number, for example, N, of rows ofcrystal positions have been labelled, splines may be utilized to curvefit these N rows of crystal positions. A crystal central position z′that lies below and near the set of N splines may be chosen, and thecrystals lying within the same row as z′ may be identified and labelled.As a further example, after a certain number, for example, K, of columnsof crystal positions have been labelled, splines may be utilized tocurve fit these K columns of crystal positions. A crystal centralposition r that lies to the left of and near the set of K splines may bechosen, and the crystals lying within the same column as the crystalcentral position r may be identified and labelled. As a still furtherexample, after a certain number, for example, K, of columns of crystalpositions have been labelled, splines may be utilized to curve fit theseK columns of crystal positions. A crystal central position r′ that liesto the right of and near the set of K splines may be chosen, and thecrystals lying within the same column as the crystal central position r′may be identified and labelled.

Alternatively, the labelling of rows and/or columns of crystals in aprogressive way may be undergone in the following way. Merely by way ofexample, after a certain number, for example, N, of rows and columns ofcrystal positions have been labelled, splines may be utilized to curvefit the N rows and columns of crystal positions respectively, forming 2Nsplines. A crystal central position z that lies on the reference columnand near the set of 2N splines may be chosen, and the crystals lyingwithin the same row as the crystal central position z may be identifiedand labelled. Similarly, a crystal central position r that lies on thereference row and near the set of 2N splines may be chosen, and thecrystals lying within the same column as the crystal central position rmay be identified and labelled.

In step 604, a judgment may be made as to whether there are ghostartifacts in the labelled rows and/or columns. A ghost artifact may be afake response caused by the interactions between neighbor detectorblocks. In some embodiments, the existence of a ghost artifact may bedetermined by comparing the number of labelled rows (or columns) withthe actual number of rows (or columns) in the detector block. If thenumber of labelled columns/rows exceeds the number of actualcolumns/rows, a ghost artifact (e.g., a ghost column/row) may be deemedto exist.

In step 605, ghost artifacts may be removed from labelled rows and/orcolumns. A ghost artifact may include a ghost spot, a ghost columnincluding multiple ghost spots, a ghost row including multiple ghostspots, etc. A ghost spot may represent an intensity spot in a floodhistogram that does not correspond to an actual crystal in a detectorblock. In some embodiments, ghost spots may be removed one by one. Insome embodiments, multiple ghost spots may be removed column by column,and/or row by row. A method of energy weighting may be applied to removea row or a column containing ghost spots. The removal of a ghostcolumn/row may be repeated until the number of labelled columns/rowsequals the number of actual columns/rows in the detector block.

In step 606, a labelled CCPM may be generated based on the labelled rowsand labelled columns. A labelled CCPM may have the actual number of rowsand actual number of columns.

In step 607, a template may be generated based on the labelled CCPM.Curve fitting may be applied on labelled rows to form row lines, say, byusing splines. Similarly, curve fitting may be applied on labelledcolumns to form column lines, by using splines. A template with smoothrow lines and smooth column lines may be generated by regularizing therow lines and column lines of the labelled CCPM. In some embodiments,Gaussian smoothing may be used to regularize or smooth the templateand/or adjust the rows and columns. A template may be generated byseveral iterations of regularization by way of, for example, Gaussiansmoothing, etc. It should be noted that the above description aboutgenerating template is merely an example, and should not be understoodas the only embodiment.

In step 608, a corrected CCPM may be generated based on the template andthe initial CCPM. The corrected CCPM may be output in step 609.

FIG. 7 is a flowchart illustrating an exemplary process for labellingrows and columns in a CCPM according to some embodiments of the presentdisclosure. Process 700 may be performed by processing logic includinghardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions run on a processingdevice to perform hardware simulation), or a combination thereof. Insome implementations, process 700 may be performed by one or moreprocessing devices (e.g., crystal lookup table module 220 of FIG. 2)executing the crystal central position generator 310 as described inconnection with FIGS. 3-4 above and elsewhere in the present disclosure.

In step 701, a crystal central position x in a flood histogram H_0 maybe retrieved. A transformed histogram by polar transformation centeredat the crystal central position x may be generated based on a floodhistogram in step 702. An intensity distribution diagram based on thetransformed histogram may be generated by calculating the projectedintensity along a line emanating from the crystal central position xwith an inclination angle θ in step 703. The abscissa of the diagram maybe the radian θ (0≦θ≦π), and the ordinate may indicate the projectedintensity of the corresponding radian. The projected intensity mayreflect the number of crystals in the projection direction. Theprojected intensity may be (e.g., positively) proportional to the numberof crystals projected along the direction of projection.

The row index of the reference row and the column index of the referencecolumn (described below) may be determined and labelled in step 704. Twovalues of the angle θ, denoted as θ_1 and θ_2, where the projectedintensities reach local minimum values, may be specified. The two linesL_1 and L_2 emanating from the crystal central position x at inclinationangles θ_1 and θ_2 may be used as reference lines for determining thereference row and the reference column. A reference row may refer to therow including the crystal central position x and one or more othercrystal central positions. A reference column may refer to the columnincluding the crystal central position x and one or more other crystalcentral positions.

Now a reference row and a reference column crossing x in H_0 may beformed. For example, a threshold d may be specified. For a crystalcentral position y different from x, the distance between y and L_1 maybe calculated. If the distance between y and L_1 is smaller than thethreshold d, then y may be considered to belong to the reference row. Areference column may be determined similarly. Specifically, a thresholdd′ may be specified first. For a crystal central position y differentfrom x, the distance between y and L_2 may be calculated. If thedistance between y and L_2 is smaller than the threshold d′, then y maybe considered to belong to the reference column. In some embodiments,the two selected angles θ_1 and θ_2 may be approximately 90 degreesapart, for example, 90±ε degrees apart. The parameter ε may be an angleerror. In some embodiments, ε may be set to be less than 10 degrees, orless than 20 degrees, or less than 30 degrees.

In step 705, the labelling of the remaining crystal rows and columns maybe performed in a progressive way. Merely by way of example, after acertain number, for example, N, of rows of crystal central positionshave been labelled, splines may be utilized to curve fit these N rows ofcrystal central positions. A crystal central position z that lies aboveand near the set of N splines may be chosen, and the crystal centralpositions lying within the same row as the crystal central position zmay be identified and labelled. Alternatively, a crystal centralposition z′ that lies below and near the set of N splines may be chosen,and the crystal central positions lying within the same row as thecrystal central position z′ may be identified and labelled. The rowcontaining the crystal central position z thus formed lying above theset of N splines may be identified first. Alternatively, the rowcontaining the crystal central position z′ thus formed lying below theset of N splines may be identified first. In some embodiments, the rowsthus formed lying above and below the set of N splines may be identifiedconcurrently.

As a further example, after a certain number, for example, K, of columnsof crystal positions have been labelled, splines may be utilized tocurve fit these K columns of crystal central positions. A crystalcentral position t that lies to the left of and near the set of Ksplines may be chosen, and the crystal central positions lying withinthe same column as the crystal central position t may be identified andlabelled. Alternatively, a crystal central position t′ that lies to theright of and near the set of K splines may be chosen, and the crystalcentral positions lying within the same column as the crystal centralposition t′ may be identified and labelled. The column containing thecrystal central position t thus formed lying to the left of the set of Ksplines may be identified first. Alternatively, the column containingthe crystal central position t′ thus formed lying to the right of theset of K splines may be identified first. In some embodiments, thecolumns thus formed lying to the left and right of the set of K splinesmay be identified concurrently.

Alternatively, the labelling of the remaining rows and/or columns ofcrystals in a progressive way may be undergone in the following way.Merely by way of example, after a certain number, for example, N, ofrows and columns of crystal central positions have been labelled,splines may be utilized to curve fit the N rows and the N columns ofcrystal central positions respectively, forming 2N splines. A crystalcentral position z that lies on the reference column and near the set of2N splines may be chosen, and the crystal central positions lying withinthe same row as the crystal central position z may be identified andlabelled. Similarly, a crystal central position r that lies on thereference row and near the set of 2N splines may be chosen, and thecrystal central positions lying within the same column as the crystalcentral position r may be identified and labelled.

The progression of labelling the crystal rows and columns for remainingcrystal central positions may be terminated when all crystal centralpositions have been traversed. Merely by way of example, the way oftraversing may be traversing the crystal central positions that lieabove or below the splines formed by the labelled rows until all suchcrystal central positions have been exhausted. For another example, theway of traversing may be traversing the crystal central positions thatlie to the left or right of the splines formed by the labelled columnsuntil all such crystal central positions have been exhausted.

In some embodiments, a procedure of removing artifacts from labelledrows and/or columns may be performed. For example, the procedure ofremoving artifacts, as described in FIG. 6, may be applied to remove theartifacts from labelled rows and/or labelled columns. The procedure ofremoving artifacts from labelled rows and/or columns may or may not beperformed. A labelled CCPM may then be generated based on the labelledrows and columns in step 706.

FIG. 8 is a flowchart illustrating an exemplary process for labellingrows and columns in a CCPM according to some embodiments of the presentdisclosure. Process 800 may be performed by processing logic includinghardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions run on a processingdevice to perform hardware simulation), or a combination thereof. Insome implementations, process 800 may be performed by one or moreprocessing devices (e.g., crystal lookup table module 220 of FIG. 2)executing the crystal central position generator 310 as described inconnection with FIGS. 3-4 above and elsewhere in the present disclosure.

In step 801, a flood histogram may be subject to Hough transformation togenerate a transformed image. The transformed image may include aplurality of spots. The value of a spot in the transformed image mayrepresent the probability of the spot located at the center of thetransformed image. In some embodiments, spots with the largest valuesmay be selected as a reference row and a column. That is, a referencerow and a reference column of the transformed image may be labelled instep 802.

In step 803, the labelling of the remaining crystal rows and columns maybe performed in a progressive way. Merely by way of example, after acertain number, for example, N, of rows of crystal central positionshave been labelled, splines may be utilized to curve fit these N rows ofcrystal central positions. A crystal central position z that lies aboveand near the set of N splines may be chosen, and the crystal centralpositions lying within the same row as the crystal central position zmay be identified and labelled. Alternatively, a crystal centralposition z′ that lies below and near the set of N splines may be chosen,and the crystal central positions lying within the same row as thecrystal central position z′ may be identified and labelled. The rowcontaining the crystal central position z thus formed lying above theset of N splines may be identified first. Alternatively, the rowcontaining the crystal central position z′ thus formed lying below theset of N splines may be identified first. In some embodiments, the rowsthus formed lying above and below the set of N splines may be identifiedconcurrently.

As a further example, after a certain number, for example, K, of columnsof crystal positions have been labelled, splines may be utilized tocurve fit these K columns of crystal central positions. A crystalcentral position t that lies to the left of and near the set of Ksplines may be chosen, and the crystal central positions lying withinthe same column as the crystal central position t may be identified andlabelled. Alternatively, a crystal central position t′ that lies to theright of and near the set of K splines may be chosen, and the crystalcentral positions lying within the same column as the crystal centralposition t′ may be identified and labelled. The column containing thecrystal central position t thus formed lying to the left of the set of Ksplines may be identified first. Alternatively, the column containingthe crystal central position t′ thus formed lying to the right of theset of K splines may be identified first. In some embodiments, thecolumns thus formed lying to the left and right of the set of K splinesmay be identified concurrently.

Alternatively, the labelling of the remaining rows and/or columns ofcrystals in a progressive way may be undergone in the following way.Merely by way of example, after a certain number, for example, N, ofrows and columns of crystal central positions have been labelled,splines may be utilized to curve fit the N rows and the N columns ofcrystal central positions respectively, forming 2N splines. A crystalcentral position z that lies on the reference column and near the set of2N splines may be chosen, and the crystal central positions lying withinthe same row as the crystal central position z may be identified andlabelled. Similarly, a crystal central position r that lies on thereference row and near the set of 2N splines may be chosen, and thecrystal central positions lying within the same column as the crystalcentral position r may be identified and labelled.

The progression of labelling the crystal rows and columns for remainingcrystal central positions may be terminated when all crystal centralpositions have been traversed. Merely by way of example, the way oftraversing may be traversing the crystal central positions that lieabove or below the splines formed by the labelled rows until all suchcrystal central positions have been exhausted. For another example, theway of traversing may be traversing the crystal central positions thatlie to the left or right of the splines formed by the labelled columnsuntil all such crystal central positions have been exhausted.

In some embodiments, a procedure of removing artifacts from labelledrows and/or columns may be performed. For example, the procedure ofremoving artifacts, as described in FIG. 6, may be applied to remove theartifacts from labelled rows and/or labelled columns. The procedure ofremoving artifacts from labelled rows and/or columns may or may not beperformed. A labelled CCPM may then be generated based on the labelledrow and the labelled column in step 804.

The above description of the central cross of FIGS. 6-8 is merely forexemplary purposes, should not be understood as the only embodiments,and these examples do not limit the scope of some embodiments of thepresent disclosure. For example, the reference row and column may berhombus, square, rectangle, or the like, or any combination thereof.

FIG. 9A is a flowchart illustrating an exemplary process for generatinga CCPM according to some embodiments of the present disclosure.

Process 900 may be performed by processing logic including hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device to performhardware simulation), or a combination thereof. In some implementations,process 900 may be performed by one or more processing devices (e.g.,crystal lookup table module 220 of FIG. 2) executing the crystal centralposition generator 310 as described in connection with FIGS. 3-4 aboveand elsewhere in the present disclosure.

In step 901, a normalized image I₀ may be generated by normalizing aflood histogram. In some embodiments, a normalized image may begenerated by linearly normalizing the intensities of the pixels in theflood histogram into a range of 0 and 1.

In some embodiments, a threshold may be specified for normalizing theflood histogram. For example, a threshold V may be specified, and acut-off procedure may be performed as follows. For a pixel whoseintensity is above V, the intensity of the pixel is set to be V. For apixel whose intensity is less than or equal to V, the intensity of thepixel may remain intact. In some embodiments, the threshold may be usedto get rid of outliers. Merely by way of example, the threshold V may beset to be a certain value below the maximum intensity of the floodhistogram. For example, the threshold V may be a value below the maximumintensity such that the intensity of certain percentage, say 96%, ofpixels of the flood histogram lies below V.

In some embodiments, a linear transformation may be performed after thecut-off procedure described above for normalizing the flood histogram.For example, the linear transformation may set the intensities of pixelsof the transformed flood histogram to between zero and one.

In step 902, multiple processed images I_(i) are generated by iterationsbased on the normalized image. In some embodiments, the normalized imagemay be used as a decay mask. The mask may be iteratively multiplied withitself. Iterative multiplications may cause the decay of the intensitiesin non-maximum regions, including artifacts such as noise and ghosts.After a certain number of iterations, intensities of one or more regionsmay approach 0, and peaks may be decayed to fewer pixels. In someembodiments, the number of iterations may be, for example, 50.

In step 903, a set of binary images SB may be generated by thresholdingalgorithm based on the processed images generated in step 902. Connectedcomponents may be identified based on binary images in step 904. Thecenter of mass of the connected components may be calculated bycalculating the average X coordinate and the average Y coordinate of thepixels in the connected components, respectively. Iterativemultiplications may cause certain low-energy spots to decay ordisappear. To prevent removing spots corresponding to actual crystals,after each iteration, thresholding and connected-component analysis maybe applied to identify a set of connected components. The thresholdingalgorithm may include Otsu thresholding, maximum entropy, isodataalgorithm, or the like, or any combination thereof. The value of or eachpixel in the flood histogram may be set as 1 or 0 based on thethresholding algorithm.

In step 905, a judgment may be made regarding whether connectedcomponents overlap. Overlapping connected components may include one ormore spots exist in different connected components. Overlappingconnected components appearing in most generations of binary images maybe reserved in step 906. In some embodiments, a connected component maybe a candidate for a peak. A candidate may appear for multiple times inmultiple generations of binary images. However, the candidate may bereserved for only one time before it disappears in a binary image. Inthis way, peaks may be detected only once in multiple generations ofbinary images (e.g., 50 decayed images). A candidate that disappearslater may be more reliable than a candidate that disappears sooner. Insome embodiments, a candidate that disappears from a binary sooner thana threshold may be deemed to be noise or an artifact. For instance, acandidate that disappears from the fifth generation of binary image orsooner may be deemed as noise or an artifact; a candidate that appearsin at least five generations of binary images may be deemed as a truepeak corresponding to an actual crystal in a detector block. It shouldbe noted that step 904, step 905, and step 906 may be performsimultaneously for avoiding repetitive computation. In some embodiments,the operations described in steps 904 may be performed on an areaincluding a plurality of connected components, instead of individualconnected components, in order to accelerate the process.

In step 907, a CCPM may be generated based on the detected peaks. Insome embodiments, a CCPM may be generated by identifying crystal centralpositions in the flood histogram. The crystal central positions mayinclude central positions of the overlapping connected components andcentral positions of the non-overlapping connected components.

FIG. 9B is an illustration of candidate crystal central position mapsimages produced by successive iterations of non-maximum decay (SNMD)algorithm according to some embodiments of the present disclosure. Thesenine images in FIG. 9B represent intermediate binary images produced bysuccessive iterations of non-maximum decay (SNMD) algorithm, with thenumber of iterations being in turn 1, 5, 10, 15, 20, 25, 30, 35, 40,respectively. It may be seen from FIG. 9B that as the number ofiteration grows, the merged crystals may become separated, and the peakof a crystal central position may grow sharper and clearer, as is shownbetween the two circled areas 908 a and 908 a′, or between 908 b and 908b′.

FIG. 10 illustrates a flowchart of an exemplary process for delineatingthe boundaries between crystals according to some embodiments of thepresent disclosure. In step 1001, a labelled crystal central positionmap may be retrieved. In step 1002, the model may be specified.Exemplary models may include a model for the external energy, a modelfor the internal energy, and/or a model for the total energy may bespecified. For example, the model for the external energy may be I(x),where I(x) may be the gray level value of the pixel x in the floodhistogram. As another example, the model for the external energy may bebI(x), where b is a positive number ranging from 0.1 to 100. As afurther example, the model for the internal energy may be d(x, x′),where d(x, x′) is the distance function between the pixels x and x′. Themodel for the total energy T(x, x′) between the pixels x and x′ may bea*d(x, x′)+s*I(x), where a and s may be the weighting factors of thetotal energy. Merely by way of example, a may be less than 0.2, and s bemay be larger than 1.4.

In step 1003, a region between a pair of adjacent splines may bedesignated. For example, two adjacent row splines formed byspline-fitting two adjacent rows of crystal central positions, may bechosen, then the region enclosed by the two adjacent row splines may bedesignated. For another example, two adjacent column splines, i.e.splines curve-fitting a column of crystal central positions, may bechosen, then the region enclosed by the two adjacent column splines maybe designated.

In step 1004, a path with a minimal cumulative energy may be determined.The path may be from left to right if the splines chosen in step 1003are row splines, and the path may be from top to bottom if the splineschosen in step 1003 are column splines. The cumulative energy of thepath may be the sum of internal energies of pixels located within thepath together with the sum of external energies between adjacent pixelslocated within the path.

To determine a path with minimal cumulative energy, first the designatedregion may be divided by w stages. Specifically, two points with thesame X coordinate may be connected together to form a stage. By choosingw different X coordinates, w stages may be formed. In some embodiments,the number of stages w may be the same as the number of pixels in a rowof flood histogram. In some embodiments, the number of stages w may bethe same as the number of pixels in a column of flood histogram. Thenumber of stages w may be no less than the average width of the crystalsin a detector block. In some embodiments, w may be larger than theaverage width of the crystals to achieve a desired smoothness of aboundary line. For example, w may be the width of the crystal centralposition map.

In some embodiments, for the w stages specified, the stage on the utterleft hand side may be designated as the zero-th stage s_0, and the stageto the right hand side of s_0 may be designated sequentially, so thatthe stage on the utter right hand side may be designated as the w-thstage s_{w−1}.

A path may be considered as a series of selected pixels in thesequentially arranged stages in either the X direction or the Ydirection. Each pixel may have an external energy. The external energymay relate to the intensity value of the pixel. For example, theexternal energy of a pixel may be linearly proportional to the intensityof the pixel. As another example, the external energy of a pixel may bequadratically proportional to the intensity of the pixel. Each pixel mayhave an internal energy. The internal energy may be the distance betweenthis pixel and the pixel that lies on the intersection of the path withthe last stage s_{w−1}.

For each pixel on stage i, accumulative energy of all pixels from stagezero up to stage i−1 may be calculated. Then the path from stage i toi−1 with minimal accumulative energy may be recorded.

On the final stage, determine the pixel with minimal accumulativeenergy. From this pixel, retrieve the path by combining recorded pathsstage by stage until the stage 0.

In step 1005, a crystal lookup table may be generated by forming theboundary line determined by designating those paths with a minimalcumulative energy. (See FIG. 13J for an illustration of a crystal lookuptable and the associated boundary lines within the crystal lookuptable).

FIG. 11 is a flowchart illustrating an exemplary process for generatinga CLT based on fusion of CCPMs generated using different methodsaccording to some embodiments of the present disclosure. Process 1100may be performed by processing logic including hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions run on a processing device to performhardware simulation), or a combination thereof. In some implementations,process 1100 may be performed by one or more processing devices (e.g.,crystal lookup table module 220 of FIG. 2) executing the crystal lookuptable generator 320 as described in connection with FIGS. 3-4 above andelsewhere in the present disclosure.

A flood histogram may be obtained in step 1101 and different CCPMs maybe generated using at least two different methods in step 1102. Theflood histogram may be received from one or more scan systems (e.g.,system 100 of FIG. 1), storage module 240, a storage device (e.g., afloppy disk, a hard disk, a wireless terminal, a cloud-based storagedevice, etc.), etc. Different methods of generating CCPMs may includeprocesses described in FIG. 6 through FIG. 9, or the like. A correctedCCPM may be generated by fusing different CCPMs in step 1103. A CLT maybe generated based on the corrected CCPM in step 1104.

It should be noted that the above description about methods ofgenerating a CLT based on a CCPM is merely an example, should not beunderstood as the only embodiment. In some embodiments, a CLT may begenerated based a crystal row and column map. In some embodiments, a CLTmay be generated based on a crystal boundary map. In some embodiments, aCLT may be generated based on a combination of a CCPM, a crystal row andcolumn map, and a crystal boundary map.

FIG. 12A is a flowchart illustrating an exemplary process for generatinga CCPM based on fusion of CCPMs generated by different methods accordingto some embodiments of the present disclosure. Process 1200 may beperformed by processing logic including hardware (e.g., circuitry,dedicated logic, programmable logic, microcode, etc.), software (e.g.,instructions run on a processing device to perform hardware simulation),or a combination thereof. In some implementations, process 1200 may beperformed by one or more processing devices (e.g., crystal lookup tablemodule 220 of FIG. 2) executing the crystal lookup table generator 320as described in connection with FIGS. 3-4 above and elsewhere in thepresent disclosure.

In step 1201, a fused CCPM may be generated by fusing different CCPMs.To evaluate the CCPs of the fused CCPM, CCPs may be classified intothree classes based on a confidence criterion in step 1202. In someembodiments, CCPs may be classified into two classes. In someembodiments, CCPs may be classified into more than two classes. Acrystal may be represented or identified by a position, a row index,and/or a column index. For instance, the first class of CCPs may includeCCPs having the same position, the same row indices and column indicesin different CCPMs, and the first class of CCPs may be retained. Thesecond class of CCPs may include CCPs whose positions are the same indifferent CCPMs, but row indices and column indices are the same in someCCPMs, but different in one or more other CCPMs. For instance, a CCPwhose positions are the same in two out of three different CCPMs maybelong to the second class. The third class of CCPs may include CCPswhose positions, row indices, and column indices are different amongdifferent CCPMs.

FIG. 12B illustrates three exemplary confidence criterions based oncrystal positions according to some embodiments of the presentdisclosure. The position of a crystal may be represented either by apeak or a block. As illustrated in FIG. 12B, the first confidencecriterion may be to judge the distance between the two peaks generatedby method 1 and method 2. In some embodiments, the two peaks may beconsidered to correspond to a same crystal when the distance between thetwo peaks is below a threshold, for example, 2 pixels. The secondconfidence criterion may be to judge the relationship between the peakand the block generated by method 3 and method 4, respectively. In someembodiments, the peak generated by method 3 and the block generated bymethod 4 may be considered to indicate a same crystal when the peakgenerated by method 3 lies within the range of the block generated bymethod 4. The third confidence criterion may be to judge therelationship between the block generated by method 5 and the blockgenerated by method 6. In some embodiments, the block generated bymethod 5 and the block generated by method 6 may be considered toindicate a same crystal when the overlapping area of the two blocks isabove a threshold, e.g., two-thirds, of the area of either block.

The second class of CCPs may be corrected in step 1203. In someembodiments, spline fitting may be used to correct the second class ofCCPs based on the first class of CCPs and the number and/or arrangementof crystals in a detector block. The third class of CCPs may becorrected in step 1204. In some embodiments, spline fitting may be usedto correct the third class of CCPs based on one or more of the followinginformation including the first class of CCPs, the original second classof CCPs, the corrected second class of CCPs, and the number and/orarrangement of crystals in the detector block. A corrected CCPM may begenerated based on the first class, the corrected second class, and thecorrected third class of CCPs in step 1205.

FIG. 12C illustrate the second class of CCPs according to someembodiments of the present disclosure. Four CCPMs generated by fourdifferent methods are shown in FIGS. 12C(a), 12C(b), 12C(c), and 12C(d),respectively. Three CCPs shown in the circled areas 12CI of FIG. 12C(a),12CII of FIG. 12C(b), 12CIII of FIG. 12C(c), and 12CIV of FIG. 12C(d)may be shown to consistently indicate the positions of three adjacentcrystals, while their row indices and column indices may be different.Thus, three CCPs may be classified into the second class. FIG. 12Dillustrates different behaviors of CCPs, from 12DI up to 12DIX, beforeand after the fusion according to some embodiments of the presentdisclosure. FIG. 12D (a) is an illustration of SNMD, (b) is anillustration of Fourier template registration, (c) is an illustration ofNSD, (d) is an illustration of final CCPs.

It should be noted that the above steps of the flow diagrams of FIGS.5-12A may be executed or performed in any order or sequence not limitedto the order and sequence shown and described in the figures. Also, someof the above steps of the flow diagrams of FIGS. 5-12A may be executedor performed substantially simultaneously where appropriate or inparallel to reduce latency and processing times. Furthermore, it shouldbe noted that FIGS. 5-12A are provided as examples only. At least someof the steps shown in these figures can be performed in a differentorder than represented, performed concurrently, or altogether omitted.

Example

The following example is for illustrative purposes only and should notbe interpreted as limitations of the claimed invention. There are avariety of alternative techniques and procedures available to those ofordinary skill in the art, which would similarly permit one tosuccessfully perform the intended invention.

FIG. 13A illustrates an exemplary flowchart of a process for producing acrystal lookup table (CLT) from a flood histogram according to someembodiments of the present disclosure. In step 1301, a flood histogramH_0 may be retrieved. The flood histogram may be input from a database,or produced by scanning a subject using a PET scanner. An example of aflood histogram may be seen in FIG. 13B.

In step 1302, an initial crystal central position map (CCPM) M_0 may beproduced based on the flood histogram H_0. For example, the initial CCPMmay be produced by locating the pixels with local maximum intensitieswithin H_0. An example of an initial CCPM may be found in FIG. 13C. Foranother example, a crystal peak identification algorithm, such as theone illustration in FIG. 9A, may be utilized to obtain the initial CCPM.

In step 1303, a crystal central position x may be chosen in the CCPM.The selection of the crystal central position x may start near thecentral area of the CCPM M_0. A crystal in the crystal lookup table maybe represented by crystal central position x. In step 1304, thosecrystal central positions within the same row as crystal centralposition x may be identified. Similarly, those crystal central positionswithin the same column as crystal central position x may be identified.The way of identifying the crystal central positions within the same rowas x may be found, for example, in the description of FIG. 7. In someembodiments, the way of identifying the crystal central positions withinthe same row as crystal central position x may be found in thedescription of FIG. 8. Merely by way of example, the flood histogram H_0may be transformed to polar coordinates centered at crystal centralposition x. The transformed flood histogram may be denoted as H_pol. SeeFIG. 13D for an example of a transformed flood histogram H_pol in polarcoordinates. In step 1305, an intensity projection diagram may be formedbased on the transformed flood histogram H_pol. Specifically, for eachline L emanating from crystal central position x with an inclinationangle θ, 0≦θ≦π, the projected intensity of crystals along the line L maybe calculated. Two values of the angle θ, denoted as θ_1 and θ_2, wherethe projected intensities reach local minimum values, may be specified.The two lines L_1 and L_2 emanating from crystal central position x ofinclination angles θ_1 and θ_2 may be used as reference lines fordetermining the row and column of the crystal central positions crossingcrystal central position x. See FIG. 13E for an example of an intensityprojection diagram. It may be seen from FIG. 13E that the projectedintensities of crystals reaches minimum values at approximately 10degrees and 80 degrees. The line L_1 emanating from crystal centralposition x with the inclination angle of approximately 10 degrees may bechosen as the reference line for determining the row of crystal centralpositions on which crystal central position x lies. The line L_2emanating from x with the inclination angle of approximately 80 degreesmay be chosen as the reference line for determining the column ofcrystal central positions on which x lies.

Now a row of crystal central positions and a column of crystal centralpositions crossing crystal central position x in M_0 may be formed. Forexample, a threshold d may be specified. For a crystal central positiony different from crystal central position x, the distance between y andL_1 may be calculated. If the distance between y and L_1 is smaller thanthe threshold d, then y may be considered to belong to the same row ascrystal central position x. A column of crystal central positions onwhich crystal central position x lies may be determined similarly. SeeFIG. 13F for an example of the row and the column of crystal centralpositions on which crystal central position x lies.

In step 1305, the labelling of the remaining crystal rows and columnsmay be performed in a progressive way. Merely by way of example, after acertain number, for example, N, of rows of crystal central positionshave been labelled, splines may be utilized to curve fit these N rows ofcrystal central positions. A crystal central position z that lies aboveand near the set of N splines may be chosen, and the crystal centralpositions lying within the same row as the crystal central position zmay be identified and labelled. Alternatively, a crystal centralposition z′ that lies below and near the set of N splines may be chosen,and the crystal central positions lying within the same row as thecrystal central position z′ may be identified and labelled. The rowcontaining the crystal central position z thus formed lying above theset of N splines may be identified first. Alternatively, the rowcontaining the crystal central position z′ thus formed lying below theset of N splines may be identified first. In some embodiments, the rowsthus formed lying above and below the set of N splines may be identifiedconcurrently.

As a further example, after a certain number, for example, K, of columnsof crystal positions have been labelled, splines may be utilized tocurve fit these K columns of crystal central positions. A crystalcentral position t that lies to the left of and near the set of Ksplines may be chosen, and the crystal central positions lying withinthe same column as the crystal central position t may be identified andlabelled. Alternatively, a crystal central position t′ that lies to theright of and near the set of K splines may be chosen, and the crystalcentral positions lying within the same column as the crystal centralposition t′ may be identified and labelled. The column containing thecrystal central position t thus formed lying to the left of the set of Ksplines may be identified first. Alternatively, the column containingthe crystal central position t′ thus formed lying to the right of theset of K splines may be identified first. In some embodiments, thecolumns thus formed lying to the left and right of the set of K splinesmay be identified concurrently.

Alternatively, the labelling of the remaining rows and/or columns ofcrystals in a progressive way may be undergone in the following way.Merely by way of example, after a certain number, for example, N, ofrows and columns of crystal central positions have been labelled,splines may be utilized to curve fit the N rows and the N columns ofcrystal central positions respectively, forming 2N splines. A crystalcentral position z that lies on the reference column and near the set of2N splines may be chosen, and the crystal central positions lying withinthe same row as the crystal central position z may be identified andlabelled. Similarly, a crystal central position r that lies on thereference row and near the set of 2N splines may be chosen, and thecrystal central positions lying within the same column as the crystalcentral position r may be identified and labelled.

The progression of labelling the crystal rows and columns for remainingcrystal central positions may be terminated when all crystal centralpositions have been traversed. Merely by way of example, the way oftraversing may be traversing the crystal central positions that lieabove or below the splines formed by the labelled rows until all suchcrystal central positions have been exhausted. For another example, theway of traversing may be traversing the crystal central positions thatlie to the left or right of the splines formed by the labelled columnsuntil all such crystal central positions have been exhausted.

In some embodiments, another traversing mechanism may be deployed todetermine if the crystal central positions in M_0 have been traversed,i.e. whether each crystal central position in M_0 has been assigned to arow of crystal central positions and a column of crystal centralpositions. In some embodiment, the traversing mechanism may beimplemented by choosing a crystal central position x in M_0 first, andthen label the reference column and reference row crossing x. Then twoadjacent columns to the reference columns may be identified and labelledsecondly, followed by the identification and labelling of two adjacentrows to the reference rows. This procedure of identifying-and-labellingmay be performed repeatedly by identifying and labelling the twoadjacent columns, followed by the identifying and labelling of the twoadjacent row until all crystal central positions have been traversed.

In some embodiments, the traversing mechanism may be performed using aconfiguration template. The configuration template may take a shape of arhombus, a rectangle, a triangle, or a polygon on which the verticeshave been designated an order of traversing. For example, if a templatehaving the shape of a rhombus is provided, and x lies at the center ofthe rhombus, then the order of traversing may be that the crystalcentral positions closest to x along reference row and reference columnmay be traversed first to determine to which row or column they belongin M_0.

After the crystal central positions in a CCPM have been traversed instep 1305, each crystal central position in the CCPM may be assigned arow and column. A procedure of artifacts removal may be performedthereafter. Specifically, if the number of columns obtained is largerthan the actual number of columns, then one or more ghost columns mayhave been obtained and need to be removed. Similarly, if the number ofrows obtained is larger than the actual number of rows, then one or moreghost rows may have been obtained and need to be removed. An exemplaryprocedure of removing ghost columns and/or ghost rows has been disclosedin the description of FIG. 6 and elsewhere in the present disclosure.

In step 1306, each remaining (after removal of ghost column(s)/row(s))crystal central position z may be assigned a row index i, and a columnindex j, indicating that the crystal central position z lies in the i-throw and j-th column in M_0. The crystal central position map with eachremaining crystal central position z being assigned its row index andcolumn index may be referred to as a labelled CCPM M_1. (See FIG. 13Gfor an illustration on the distribution of rows in the labelled CCPM,and see FIG. 13H for an illustration on the distribution of columns inthe labelled CCPM).

In step 1307, a template T_0 may be generated based on the labelled CCPMM_1. In some embodiments, the template T_0 may be produced bycurve-fitting the rows and the columns of crystal central positions inM_1 by splines. The template may take the form of a grid. The choice ofsplines may be based on the desired smoothness and the desired order ofthe spline function. For example, a third order spline may be utilizedto form the template. See FIG. 13I for an illustration of a templateT_0.

In step 1308, the crystal central position map M_0 may be correctedusing the template T_0 by regularizing the template T_0 iteratively. Forexample, T_0 may be modified by smoothing the splines according to thedesired smoothness to generate a smoothened template T_1. This processmay be referred to as smoothing.

The crystal central position map M_0 may then be compared to thesmoothened template T_1. If the cross point x of splines forming a rowand a column of T_1 does not correspond to a crystal central position inM_0, the cross point x may be replaced by its closest crystal centralposition x′ in M_0. The same row index and the column index for x may beassigned to x′. A template T_2 may be generated by curve-fitting thex's. The process of generating T_2 by comparing M_0 with T_1 tore-locate the cross points in M_0 may be referred to ascross_points_picking_and_fitting.

The process of regularizing T_0 may be accomplished by repeating theprocesses of smoothing and cross_point_picking_and_fitting until adesired smoothness of splines is achieved. For example, the desiredsmoothness of splines may be such that the maximum absolute value of thethird order derivative of the splines is less than a threshold.

The template obtained after regularizing T_0 may be referred to asT_final, and the crystal central position map given by the cross pointsin T_final may be referred to as M_final.

In step 1309, a crystal lookup table (CLT) may be produced based onM_final. Specifically, for the crystal central positions within M_final,the procedure specified in FIG. 10 for delineating the boundariesbetween crystals may be employed. The boundary lines thus produced basedon M_final may provide a crystal lookup table that describes thecorrespondence between pixels in H_0 with crystals in a detector block.

The example described above are provided to give those of ordinary skillin the art a complete disclosure and description of how to make and usethe embodiments of the arrangements, devices, compositions, systems andmethods of the disclosure, and are not intended to limit the scope ofwhat the inventors regard as their disclosure. For example, the methodof generating a crystal lookup table shown in FIG. 13A may be used asone of the multiple methods for fusing the multiple CCPMS as explainedin FIG. 11. All patents and publications mentioned in the specificationare indicative of the levels of skill of those skilled in the art towhich the disclosure pertains.

As will be also appreciated, the above described method embodiments maytake the form of computer or controller implemented processes andapparatuses for practicing those processes. The disclosure may also beembodied in the form of computer program code containing instructionsembodied in tangible media, such as floppy diskettes, CD-ROMs, harddrives, or any other computer-readable storage medium, wherein, when thecomputer program code is loaded into and executed by a computer orcontroller, the computer becomes an apparatus for practicing theinvention.

The disclosure may also be embodied in the form of computer program codeor signal, for example, whether stored in a storage medium, loaded intoand/or executed by a computer or controller, or transmitted over sometransmission medium, such as over electrical wiring or cabling, throughfiber optics, or via electromagnetic radiation, wherein, when thecomputer program code is loaded into and executed by a computer, thecomputer becomes an apparatus for practicing the invention. Whenimplemented on a general-purpose microprocessor, the computer programcode segments configure the microprocessor to create specific logiccircuits.

Computer program code for carrying out operations for aspects of someembodiments of the present disclosure may be written in any combinationof one or more programming languages, including an object orientedprogramming language such as Java, C++, C#, VB, Scala, Smalltalk,Eiffel, JADE, Emerald, NET, Python or the like, conventional proceduralprogramming languages, such as the “C” programming language, VisualBasic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programminglanguages such as Python, Ruby and Groovy, or other programminglanguages. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider) or ina cloud computing environment or offered as a service such as a Softwareas a Service (SaaS).

While the invention has been described with reference to variousembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from theessential scope thereof. Therefore, it is intended that the inventionnot be limited to the particular embodiment disclosed as the best modecontemplated for carrying out this invention, but that the inventionwill include all embodiments falling within the scope of the appendedclaims.

Some portions of the detailed descriptions which follow are presented interms of algorithms and symbolic representations of operations on databits within a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout the description,discussions utilizing terms such as “sending,” “receiving,”“generating,” “providing,” “calculating,” “executing,” “storing,”“producing,” “determining,” “reducing,” “registering,” “reconstructing,”or the like, refer to the action and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage, transmission or display devices.

The terms “first,” “second,” “third,” “fourth,” etc. as used herein aremeant as labels to distinguish among different elements and may notnecessarily have an ordinal meaning according to their numericaldesignation.

In some implementations, any suitable computer readable media can beused for storing instructions for performing the processes describedherein. For example, in some implementations, computer readable mediacan be transitory or non-transitory. For example, non-transitorycomputer readable media can include media such as magnetic media (suchas hard disks, floppy disks, etc.), optical media (such as compactdiscs, digital video discs, Blu-ray discs, etc.), semiconductor media(such as flash memory, electrically programmable read only memory(EPROM), electrically erasable programmable read only memory (EEPROM),etc.), any suitable media that is not fleeting or devoid of anysemblance of permanence during transmission, and/or any suitabletangible media. As another example, transitory computer readable mediacan include signals on networks, in connectors, conductors, opticalfibers, circuits, and any suitable media that is fleeting and devoid ofany semblance of permanence during transmission, and/or any suitableintangible media.

Accordingly, methods, systems, and media for image reconstruction areprovided. Although the disclosed subject matter has been described andillustrated in the foregoing illustrative implementations, it isunderstood that some embodiments of the present disclosure has been madeonly by way of example, and that numerous changes in the details ofimplementation of the disclosed subject matter can be made withoutdeparting from the spirit and scope of the disclosed subject matter.

The entire disclosure of each document cited (including patents, patentapplications, journal articles, abstracts, laboratory manuals, books, orother disclosures) in the Background, Summary, Detailed Description, andExamples is hereby incorporated herein by reference. All referencescited in this disclosure are incorporated by reference to the sameextent as if each reference had been incorporated by reference in itsentirety individually. However, if any inconsistency arises between acited reference and some embodiments of the present disclosure, someembodiments of the present disclosure takes precedence.

The terms and expressions which have been employed herein are used asterms of description and not of limitation, and there is no intention inthe use of such terms and expressions of excluding any equivalents ofthe features shown and described or portions thereof, but it isrecognized that various modifications are possible within the scope ofthe disclosure claimed. Thus, it should be understood that although thedisclosure has been specifically disclosed by preferred embodiments,exemplary embodiments and optional features, modification and variationof the concepts herein disclosed can be resorted to by those skilled inthe art, and that such modifications and variations are considered to bewithin the scope of this disclosure as defined by the appended claims.

When a Markush group or other grouping is used herein, all individualmembers of the group and all combinations and possible sub-combinationsof the group are intended to be individually included in the disclosure.Every combination of components or materials described or exemplifiedherein can be used to practice the disclosure, unless otherwise stated.One of ordinary skill in the art will appreciate that methods, deviceelements, and materials other than those specifically exemplified can beemployed in the practice of the disclosure without resort to undueexperimentation. All art-known functional equivalents, of any suchmethods, device elements, and materials are intended to be included inthis disclosure. Whenever a range is given in the specification, forexample, a temperature range, a frequency range, a time range, or acomposition range, all intermediate ranges and all subranges, as wellas, all individual values included in the ranges given are intended tobe included in the disclosure. Any one or more individual members of arange or group disclosed herein can be excluded from a claim of thisdisclosure. The disclosure illustratively described herein suitably canbe practiced in the absence of any element or elements, limitation orlimitations that is not specifically disclosed herein.

What is claimed is:
 1. A method comprising: receiving a flood histogramrelating to a subject; determining a crystal central position map basedon the flood histogram, the crystal central position map comprising aplurality of crystal central positions; forming rows and columns of theplurality of crystal central positions to generate a labelled crystalcentral position map; forming a template based on the rows and thecolumns in the labelled crystal central position map; and correcting thelabelled crystal central position map based on the template to obtain acorrected crystal central position map.
 2. The method of claim 1, thedetermining the crystal central position map based on the floodhistogram comprising: performing a zero-one normalization on the floodhistogram to generate a normalized flood histogram I_0; performing Kiterations of decay mask treatment on I_0 to generate a series ofintermediate flood histogram I_i, 1≦i≦K; for each of the intermediateflood histogram I_i, performing a threshold determination on I_i togenerate a binary image B_i; and generating the crystal central positionmap based on a group comprising the binary images B_i, 1≦i≦K.
 3. Themethod of claim 2, the performing a zero-one normalization on the floodhistogram comprising: performing a linear transformation on the floodhistogram to obtain a transformed flood histogram, the intensities ofthe transformed flood histogram falling in a range between zero and one.4. The method of claim 2, the decay mask treatment on an imagecomprising: multiplying the image by the normalized flood histogram I_0.5. The method of claim 2, the performing a threshold determination onI_i comprising performing an Otsu thresholding algorithm on I_i.
 6. Themethod of claim 2, the generating a crystal central position mapcomprising: setting an initial collection of connected components C tobe void; for each B_i, i=K, K−1, . . . , 2, 1, identifying a pluralityof connected components in B_i; classifying the connected components inB_i as overlapping or non-overlapping with respect to C; collecting theset of non-overlapping connected components in B_i into C; and creatingthe crystal central position map based on C.
 7. The method of claim 6further comprising: for each of the connected components in C,identifying a center of mass of the connected component as a crystalcentral position in the flood histogram.
 8. The method of claim 1, thedetermining the crystal central position map comprising calculating alocal maximum gray level of the flood histogram.
 9. The method of claim1, the forming rows and columns comprising performing Houghtransformation on the flood histogram to generate a transformed image;selecting a configuration template; for a first crystal central positionof the plurality of crystal central positions, identifying one or morecandidate crystal central positions based on the selected configurationtemplate and the transformed image; and marking the one or morecandidate crystal central positions as in a same row or a same column asthe first crystal central position.
 10. The method of claim 1, thecorrecting the labelled crystal central position map based on thetemplate comprising: iteratively regularizing the template.
 11. Anon-transitory computer readable medium comprising executableinstructions that, when executed by at least one processor, cause the atleast one processor to effectuate a method comprising: receiving a floodhistogram relating to a subject; determining a crystal central positionmap based on the flood histogram, the crystal central position mapcomprising a plurality of crystal central positions; forming rows andcolumns of the plurality of crystal central positions to generate alabelled crystal lookup table; forming a template based on the rows andthe columns in the labelled crystal central position map; and correctingthe labelled crystal central position map based on the template toobtain a corrected crystal central position map.
 12. The non-transitorycomputer readable medium of claim 11, the determining the candidatecrystal central position map based on the flood histogram comprising:performing a zero-one normalization on the flood histogram to generate anormalized flood histogram I_0; performing K iterations of decay masktreatment on I_0 to generate a series of intermediate flood histogramI_i, 1≦i≦K; for each of the intermediate flood histogram I_i, performinga threshold determination on I_i to generate a binary image B_i; andgenerating the crystal central position map based on a group comprisingthe binary images B_i, 1≦i≦K.
 13. The method of claim 12, the performinga zero-one normalization on the flood histogram comprising: performing alinear transformation on the flood histogram so that the range oftransformed intensity is between zero and one.
 14. The method of claim12, the decay mask treatment on an image comprising: multiplying theimage by the normalized flood histogram.
 15. The method of claim 12, theperforming automatic threshold determination on I_i further comprising:performing an Otsu thresholding algorithm.
 16. The method of claim 12,the generating a crystal central position map based on the binary imagesB_i, 1≦i≦K comprising: setting the initial collection of connectedcomponents C to be void; for each B_i, i=K, K−1, . . . , 2, 1,identifying a plurality of connected components in B_i; classifying theconnected components in B_i as overlapping or non-overlapping withrespect to C; collecting the set of non-overlapping connected componentsin B_i into C; and creating the crystal central position map based on C.17. The method of claim 6 further comprising: for each of the connectedcomponents in C, identifying center of mass of the connected componentas a crystal central position in the flood histogram associated with theconnected component.
 18. The method of claim 11, the determining thecrystal central position map comprising: calculating a local maximumgray level of the flood histogram.
 19. The method of claim 11, theforming rows and columns comprising performing Hough transformation onthe flood histogram to generate a transformed image; selecting aconfiguration template; for a first crystal central position of theplurality of crystal central positions, identifying one or morecandidate crystal central positions based on the selected configurationtemplate and the transformed image; and marking the one or morecandidate crystal central positions as in a same row or a same column asthe first crystal central position.
 20. The method of claim 11, thecorrecting the labelled crystal central position map based on thetemplate comprising: iteratively regularizing the template.